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Modeling joint restoration strategies for interdependent infrastructure systems.

ABSTRACT: Life in the modern world depends on multiple critical services provided by infrastructure systems which are interdependent at multiple levels. To effectively respond to infrastructure failures, this paper proposes a model for developing optimal joint restoration strategy for interdependent infrastructure systems following a disruptive event. First, models for (i) describing structure of interdependent infrastructure system and (ii) their interaction process, are presented. Both models are considering the failure types, infrastructure operating rules and interdependencies among systems. Second, an optimization model for determining an optimal joint restoration strategy at infrastructure component level by minimizing the economic loss from the infrastructure failures, is proposed. The utility of the model is illustrated using a case study of electric-water systems. Results show that a small number of failed infrastructure components can trigger high level failures in interdependent systems; the optimal joint restoration strategy varies with failure occurrence time. The proposed models can help decision makers to understand the mechanisms of infrastructure interactions and search for optimal joint restoration strategy, which can significantly enhance safety of infrastructure systems.

SUBMITTER: Zhang C

PROVIDER: S-EPMC5897001 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Modeling joint restoration strategies for interdependent infrastructure systems.

Zhang Chao C Kong Jingjing J Simonovic Slobodan P SP

PloS one 20180412 4

Life in the modern world depends on multiple critical services provided by infrastructure systems which are interdependent at multiple levels. To effectively respond to infrastructure failures, this paper proposes a model for developing optimal joint restoration strategy for interdependent infrastructure systems following a disruptive event. First, models for (i) describing structure of interdependent infrastructure system and (ii) their interaction process, are presented. Both models are consid ...[more]

PMID: 29649300

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Project description:Due to the ubiquitous nature of disruptive extreme events, functionality of the critical infrastructure systems (CIS) is constantly at risk. In case of a disruption, in order to minimize the negative impact to the society, service networks operating on the CIS should be restored as quickly as possible. In this paper, we introduce a novel network science inspired measure to quantify the criticality of components within a disrupted service network and develop a restoration heuristic (Cent-Restore) that prioritizes restoration efforts based on this measure. As an illustrative case study, we consider a road network blocked by debris in the aftermath of a natural disaster. The debris obstructs the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites, debilitating the emergency service network. In this context, the problem is defined as finding a schedule to clear the roads with the limited resources. First, we develop a mixed-integer programming model for the problem. Then we validate the efficiency and accuracy of the Cent-Restore heuristic on randomly generated instances by comparing it to the model. Furthermore, we use Cent-Restore to recommend real-time restoration plans for disrupted road networks of Boston and Manhattan and analyze the performance of the plans over time through resilience curves. We compare Cent-Restore to the current restoration guidelines proposed by FEMA and other strategies that prioritize the restoration efforts based on different measures. As a result we confirm the importance of including specific post-disruption attributes of the networks to create effective restoration strategies. Moreover, we explore the relationship between a service network's resilience and its topological and operational characteristics under different disruption scenarios. The methods and insights provided in this work can be extended to other disrupted large-scale critical infrastructure systems in which the ultimate goal is to enable the functions of the overlaying service networks.

Project description:BackgroundThis study examines research knowledge infrastructures (RKIs) found in health systems. An RKI is defined as any instrument (i.e., programs, interventions, tools) implemented in order to facilitate access, dissemination, exchange, and/or use of evidence in healthcare organisations. Based on an environmental scan (17 key informant interviews) and scoping review (26 studies), we found support for a framework that we developed that outlines components that a health system can have in its RKI. The broad domains are climate for research use, research production, activities used to link research to action, and evaluation.The objective of the current study is to profile the RKI of three types of health system organisations--regional health authorities, primary care practices, and hospitals--in two Canadian provinces to determine the current mix of components these organisations have in their RKI, their experience with these components, and their views about future RKI initiatives.MethodsThis study will include semistructured telephone interviews with a purposive sample region of a senior management team member, library/resource centre manager, and a 'knowledge broker' in three regional health authorities, five or six purposively sampled hospitals, and five or six primary care practices in Ontario and Quebec, for a maximum of 71 interviewees. The interviews will explore (a) which RKI components have proven helpful, (b) barriers and facilitators in implementing RKI components, and (c) views about next steps in further development of RKIs.DiscussionThis is the first qualitative examination of potential RKI efforts that can increase the use of research evidence in health system decision making. We anticipate being able to identify broadly applicable insights about important next steps in building effective RKIs. Some of the identified RKI components may increase the use of research evidence by decision makers, which may then lead to more informed decisions.

Dataset Information

Modeling joint restoration strategies for interdependent infrastructure systems.

Publications

Modeling joint restoration strategies for interdependent infrastructure systems.

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